Pathology is a very visual science. For example, cancers grow in recognizable patterns that allow for their automated identification. A melanoma has a certain growth pattern that differs from a carcinoma of the prostate. Benign conditions also have patterns. Skin rashes, for example, are diagnosed by a combination of a type of inflammatory cells and location in the skin, that is, whether the inflammation is around blood vessels, within the epidermis, scattered, etc.
A fundamental aspect of histopathology has been the recognition that a morphological appearance of tumor can be correlated with degree of malignancy. Essentially pathology involves manual pattern recognition by human pathologists. This art of pattern recognition becomes very accurate after many years of practice. These basic principles of pattern recognition are extrapolated to all tissue types and in the detection of all pathological conditions. Pathological reports generated on the given sample must be clear and comprehensible to avoid medical errors. However, there is a high degree of inter-laboratory variability in the interpretation of what is seen and perceived by pathologists through manual microscopy. One of the reasons for this inter-laboratory variability is human error, which in turn points at lack of automation tools. Use of automation tools in the pathological analysis helps reduce the variability that is often seen between different pathological laboratories.
As is known in the medical arts, diagnosis of cancer is done primarily on the basis of histologic (i.e., tissue) evaluation. Cancer identification is possible because of differential staining of tissue samples achieved by specific methods of staining such as Haematoxylin and Eosin (H/E) staining. However, for the specific diagnosis of the given type of cancer, a number of immunohistochemical (IHC) markers are used.
Estrogen plays a central role in regulating growth kinetics of a variety of epithelial linings, most importantly in the breast and endometrium. Estrogen binds to an estrogen receptor (ER), directly stimulating proliferation and differentiation. ER translocates to a cell nucleus, where it can bind to promoter sites and thus, regulates the expression of many other genes.
Estrogen also mediates part of its proliferative action on normal breast through transactivation of the progesterone receptor (PR); progesterone is also a mitogenic stimulus for mammary epithelium.
The assessment of ER and PR status in tumors by IHC has become the standard of care in breast cancers, and is rapidly being incorporated as a biomarker for other tumors as well. This analysis provides clinicians with important prognostic information, and helps predict the response to endocrine therapy.
For example, breast cancer patients whose lesions contain both ER and PR have the best probability of remission following hormonal therapy (approaching 70%) than the group of patients whose lesions contain either receptor alone (approximately 30%), or very low levels of both receptors (approximately 10%). It has been shown that tumors expressing ER and PR tend to be better differentiated and low-grade tumors, but this is not always the case. Cancer related survival in breast cancers is independently predicted by the status of ER and PR in some studies. Similarly, in the endometrium, ER negative status has been shown to be predictive of recurrence of low stage tumors, independent of tumor grade, while negative PR status is associated with a significant risk of lymph node metastasis independent of other clinicopathologic factors.
The proto-oncogene Her-2/neu (C-erbB2) has been localized to chromosome 17q and encodes a transmembrane tyrosine kinase growth factor receptor. The protein product of the Her-2/neu gene is overexpressed in 25-30% of breast cancers, and in approximately 90-95% of these cases, upregulation is a direct result of gene amplification.
A significant proportion of intraductal breast carcinomas (DCIS) demonstrate Her-2/neu amplification/overexpression, suggesting that this oncogene is activated early in the progression of malignant breast disease. Clinical studies in thousands of patients with breast cancer over the last decade have convincingly demonstrated that amplification/overexpression of Her-2/neu is associated with a poor prognosis. Additional solid tumors with amplification/overexpression of Her-2/neu include gynecologic malignancies (ovary and endometrium), and prostatic, pancreatic and hepatocellular adenocarcinomas; most studies in these malignancies also support the notion that increased Her-2/neu levels are associated with an adverse prognosis.
Cancers of the epithelial cells are the most common cancers, about 90% of the total cancers diagnosed. Therefore, identification of epithelial cells in a given digital image is a first step towards an actual identification of a cancer marker being searched for. For example, identification of ER/PR, Her2, or other markers in the breast cancer tissues. In breast cancer tissues, one specific marker searched for is ER/PR, present only in epithelial cells. Thus, a first step is to identify an epithelial part of a tissue sample. A pathologist, because of years of experience immediately differentiates an epithelial part of a tissue sample from a stromal part and looks for a specific marker. However, for a method to work on identification of a specific marker in the given tissue, it is essential to identify and differentiate the epithelial cell areas from the non-epithelial cell areas.
The importance of differentiating epithelial cell areas in a digital has multiple applications. Apart from identifying a cancer, it is critical to distinguish invasive carcinomas (IC) from noninvasive lesions. Since, cancer is life threatening when it becomes invasive, it carries a potential for spreading and metastasis. Therefore an accurate diagnosis of a presence, or absence of stromal invasion is essential.
Identification of the epithelial cell areas of a given digital image is a first step towards an automation of an entire pathological analysis through microscopy and would help in the applications such as, Nuclear pleomorphism. Mitotic Count, Tubule formation, Detection of markers stained by IHC, etc.
Using nuclear pleomorphism, manual grading of cancer comprises a very important part of the pathological analysis of cancer tissue. Cancers of the same organ could be of different types, but need to be assigned an overall grade. The results have to be accurate as it decides the prognosis and treatment of the patient. For example, breast cancer is classified on the basis of TNM system, the basis of which is a Nottingham modification of the Bloom and Richardson method of grading. The three separate parameters of this system are, Nuclear grade, Tubule formation, Mitosis.
Nuclear grade is assigned on the basis of appearance of the nucleus, its size, shape, appearance of nucleoli. Detection of nuclear pleomorphism and nucleus identification is essentially helpful in assigning a score in grading of breast cancer.
Tubule formation is checked in an entire image and differentiation of epithelial parts is helpful in assigning grades. Another important score of the grading system is the evaluation of Mitotic index of the sample. Several studies have shown that the mitotic count is the most important variable in the grading system used for the prognosis of breast cancer.
Accuracy of a detection of mitotic count is also essential. An overall grade of the neoplasm is determined by adding individual scores of the three separate parameters, tubules, nuclei and mitoses. The grading of the neoplasm has a very important role to play in the treatment and prognosis of the patient. All these parameters are searched for in epithelial cancer cells in the given image.
IHC markers, such as ER-PR quantitation is also used. In many areas of histopathology, just a broad category, such as a diagnosis of breast carcinoma, does not give enough information for the referring clinician to make decisions about patient prognosis and treatment. There are many IHC markers such as ER/PR, Her2, etc. which play a very important role in the accurate diagnosis of the cancer. For example, ER/PR assessment is important to ensure the appropriate use of hormonal therapies. It is also necessary to combine intensity staining measurement and object counting to precisely quantitative the percentage of positivity stained nuclei in the epithelial part of the tissue section.
Pathologists use their knowledge and expertise in identifying IHC patterns. Many of these properties do not have a rigid definition. Many a times pathologists give experience based decisions. However as mentioned earlier, there are several pitfalls and human error also contributes to the errors in the determination of epithelial cell count in IHC patterns.
It is observed that the seemingly simple task of epithelial cell counting becomes difficult because the counting has to be done for large number of sections. Non stained epithelial cells are difficult to identify in large IHC pattern. Problem gets even more complex if there are lymph cells of approximately same size as epithelial cell, or if some of the epithelial cells have vesicular structure. Even experienced pathologist might find it extremely difficult to count epithelial cells in a large IHC pattern.
Examination of tissue images typically has been performed manually by either a lab technician or a pathologist. In the manual method, a slide prepared with a biological sample is viewed at a low magnification under an optical microscope to visually locate IHC patterns of interest. Those areas of the slide where IHC patterns of interest are located are then viewed at a higher magnification to count epithelial cells.
An automated system that automatically analyzes digital images to which an IHC compound has been applied is expected to behave in a manner similar to human pathologist and at the same time produce consistent conclusions and/or better, conclusions with fewer errors than human pathologists.
However, there are several problems associated with using existing digital image analysis techniques for analyzing images for identifying epithelial cells in IHC patterns. One problem is that existing digital image analysis uses aggregate values over IHC patterns rather than individual epithelial cell level. Another problem is identification of IHC pattern boundaries. Standard digital image analysis based on texture alone does not provide accurate boundaries of IHC patterns. There is a need to incorporate some of the IHC properties of biological tissues in identifying accurate boundaries.
There have been attempts to solve some of the problems associated with automating manual methods for analyzing IHC samples. Automated analysis systems have been developed to improve the speed and accuracy of the IHC testing process. For example, U.S. Pat. No. 6,546,123, entitled “Automated detection of objects in a biological sample” that issued to McLaren, et al. teaches “a method, system, and apparatus are provided for automated light microscopic for detection of proteins associated with cell proliferative disorders.”
U.S. Pat. No. 5,546,323, entitled “Methods and apparatus for measuring tissue section thickness,” that issued to Bacus et al., teaches “an apparatus and method for measuring the thickness of a tissue section with an automated image analysis system, preferably using polyploid nuclear DNA content, for subsequent use in analyzing cell objects of a specimen cell sample for the diagnosis and treatment of actual or suspected cancer or monitoring any variation in the nominal thickness in a microtome setting. An image of a measurement material, such as a rat liver tissue section, having known cell object attributes is first digitized and the morphological attributes, including area and DNA mass of the cell objects, are automatically measured from the digitized image. The measured attributes are compared to ranges of attribute values which are preestablished to select particular cell objects. After the selection of the cell objects, the operator may review the automatically selected cell objects and accept or change the measured cell object attribute values. In a preferred embodiment, each selected cell object is assigned to one of three classes corresponding to diploid, tetraploid and octoploid cell morphology and the measured DNA mass of the identified cell object fragments in the rat liver tissue section sample may be corrected. Next, the selected cell objects of the measurement material, e.g., DNA Mass, are then graphically displayed in a histogram and the thickness of the rat liver tissue section can be measured based upon the distribution.”
U.S. Pat. No. 5,526,258, entitled “Method and apparatus for automated analysis of biological specimens,” that issued to Bacus teaches “an apparatus and method for analyzing the cell objects of a cell sample for the diagnosis and treatment of actual or suspected cancer is disclosed. An image of the cell sample is first digitized and morphological attributes, including area and DNA mass of the cell objects are automatically measured from the digitized image. The measured attributes are compared to ranges of attribute values which are preestablished to select particular cell objects having value in cancer analysis. After the selection of cell objects, the image is displayed to an operator and indicia of selection is displayed with each selected cell object. The operator then reviews the automatically selected cell objects, with the benefit of the measured cell object attribute values and accepts or changes the automatic selection of cell objects. In a preferred embodiment, each selected cell object is assigned to one of six classes and the indicia of selection consists of indicia of the class into which the associated cell object has been placed. The measured DNA mass of identified cell object fragments in tissue section samples may also be increased to represent the DNA mass of the whole cell object from which the fragment was sectioned.
U.S. Pat. No. 5,018,209, entitled “Analysis method and apparatus for biological specimens,” that issued to Bacus et al., teaches “a method and apparatus are provided for selecting and analyzing a subpopulation of cells or cell objects for a certain parameter such as DNA, estrogen, and then measuring the selected cells. The observer in real time views a field of cells and then gates for selection based on the morphological criteria those cells that have the visual parameter such as colored DNA or colored antigen into a subpopulation that is to be measured. The selected cells are examined by digital image processing and are measured for a parameter such as a true actual measurement of DNA in picograms. A quantitation of the measured parameter is generated and provided.”
U.S. Published Patent Application, 20030049701, entitled “Oncology tissue microarrays,” published by Muraca suggests “oncology tissue microarrays. In one aspect, the microarrays comprise a plurality of cell and/or tissue samples, each sample representing a different type of cancer. In another aspect of the invention, each sample represents a different stage of cancer. In still a further aspect of the invention, samples are ordered on the substrate of the microarray into groups according to common characteristics of the patients from whom the samples are obtained. By dividing tissue samples on the substrate into different groupings representing different tissue types, subtypes, histological lesions, and clinical subgroups, the microarrays according to the invention enable ultra-high-throughput molecular profiling.”
U.S. Published Patent Application, 20030092047, entitled “Methods of cytodiagnostic staging of neoplasia and squamous cell carcinoma,” published by LaMorte suggests “Methods of diagnosing whether an epithelial tissue is an abnormal tissue by determining an expression pattern for PML in the epithelial tissue; determining an expression pattern for nuclear bodies in the epithelial tissue; determining SUMO-1 colocalization and comparing the expression pattern for PML and the expression pattern for nuclear bodies with a control are disclosed. Also disclosed are methods for diagnosing whether a subject has mild dysplasia, moderate dysplasia, Type A severe dysplasia, Type B severe dysplasia, cervical squamous cell carcinoma, or poorly-differentiated cervical squamous cell carcinoma by determining an expression pattern for PML in an epithelial tissue sample from the subject; determining an expression pattern for nuclear bodies in the epithelial tissue; determining SUMO-1 colocalization; and determining whether the expression pattern for PML, the expression pattern for nuclear bodies, and the SUMO-1 colocalization of the epithelial tissue sample is consistent with expression patterns expected for mild dysplasia, moderate dysplasia, Type A severe dysplasia, Type B severe dysplasia, cervical squamous cell carcinoma, or poorly-differentiated cervical squamous cell carcinoma.”
U.S. Published Patent Application, 20030170703, entitled “Method and/or system for analyzing biological samples using a computer system,” published by Piper et al. suggests “a method and/or system for making determinations regarding samples from biologic sources. A computer implemented method and/or system can be used to automate parts of the analysis.”
Biogenex (www.biogenex.com) has reported products for image analysis for diagnosis and screening purposes where morphometry has been used in numerous research studies to differentiate a variety of neoplastic and non-neoplastic conditions. Cells or other structures of diagnostic interest are measured using image analysis techniques.
The ChromaVision Automated Cellular Imaging System (ACIS) (www.chromavision.com) provides automated measurements on immunohistochemically (IHC) stained tissue sections.
Applied Imaging Reasearch (www.appliedimagingcorp.com) provides automated quantification of IHC stained tissue sections.
However, these systems still do not solve all of the problems associated with automatically analyzing digital images of tissue samples to which an IHC compound has been applied.
Therefore it is desirable to provide an automation tool that can clearly differentiate an epithelial part form the non-epithelial part of digital images of tissue samples to which an IHC compound has been applied.